ML Ops Technical Product Manager
Apply NowCompany: Tata Consultancy Services
Location: Atlanta, GA 30349
Description:
- 5+ years of experience in product management,preferably with a focus on ML Ops, data science, or machine learninginfrastructure.
- Strong understanding of ML Ops tools andplatforms, including ML pipelines, CI/CD, model versioning, and monitoringframeworks.
- Technical expertise in machine learning, dataengineering, and DevOps methodologies
- Experience with cloud platforms (AWS, Azure,Google Cloud) and their ML services, data platforms like BigQuery.
- Familiarity with Agile methodologies andproject management tools (e.g., Jira, Github).
- Experience developing with containers andKubernetes in cloud computing environments
- Exposure to write, run SQL queries tovalidate data availability & quality
- Experience of reading through code repos tounderstand logic
- Experience in managing, tracking progressacross key MLOps stages: data sourcing, feature engineering, model trainingincluding hyperparameter tuning if any, testing and deployment.
- Exposure to Vertex AI, MLFlow, Kubeflow
- Exposure to implementing model governanceframeworks & reproducibility standards.
- Exposure to setting up/interpretingdashboards for model performance.
Preferred-
- Knowledge of model interpretability and explain abilitytools and techniques.
- Experience in data privacy and compliance asit relates to ML.
- Prior experience with large-scale ML systemdeployments.
Roles &Responsibilities:
- Product Strategy &Roadmap: Defineand prioritize the ML Ops product roadmap by assessing business goals, customerneeds, and emerging industry trends in ML Ops.
- Cross-functionalCollaboration: Workclosely with data scientists, ML engineers, DevOps, and software engineers toensure seamless integration and deployment of ML models.
- Project Management: Coordinate andmanage timelines, resources, and deliverables across multiple teams to keepprojects on track.
- Model LifecycleManagement: Overseethe end-to-end ML model lifecycle, including data preparation, modeldevelopment, deployment, monitoring, and maintenance.
- Automation &Scaling: Identifyopportunities for automation and scalability in the ML pipeline, from dataingestion to model deployment.
- Monitoring &Optimization: Developand implement monitoring and alerting frameworks for model performance and dataquality. Partner with engineering teams to troubleshoot and optimize pipelines.
- StakeholderCommunication: Serveas the primary point of contact for internal and external stakeholders.Communicate product updates, metrics, and results to senior leadership.
- Risk Management: Identify,assess, and mitigate risks related to ML model deployment, including ethicalconsiderations, data privacy, and regulatory compliance.
- Documentation &Training: Developclear and comprehensive documentation for ML Ops processes and workflows.Provide training to teams on best practices.
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Salary Range - $100,000-$150,000 a year